Upload 4 files

app.py

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+
+import torch
+import torch.nn as nn
+from transformers import BertTokenizer, BertModel
+import gradio as gr
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+id2label = {0: "负向", 1: "中性", 2: "正向"}
+
+class BertCNNClassifier(nn.Module):
+    def __init__(self, bert_model, num_classes=3, dropout=0.3):
+        super().__init__()
+        self.bert = bert_model
+        self.conv1 = nn.Conv2d(1, 100, (3, bert_model.config.hidden_size))
+        self.conv2 = nn.Conv2d(1, 100, (4, bert_model.config.hidden_size))
+        self.conv3 = nn.Conv2d(1, 100, (5, bert_model.config.hidden_size))
+        self.dropout = nn.Dropout(dropout)
+        self.fc = nn.Linear(300, num_classes)
+
+    def forward(self, input_ids, attention_mask, token_type_ids=None):
+        with torch.no_grad():
+            outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
+        x = outputs.last_hidden_state.unsqueeze(1)
+        x1 = torch.relu(self.conv1(x)).squeeze(3)
+        x2 = torch.relu(self.conv2(x)).squeeze(3)
+        x3 = torch.relu(self.conv3(x)).squeeze(3)
+        x1 = torch.max_pool1d(x1, x1.size(2)).squeeze(2)
+        x2 = torch.max_pool1d(x2, x2.size(2)).squeeze(2)
+        x3 = torch.max_pool1d(x3, x3.size(2)).squeeze(2)
+        x = torch.cat((x1, x2, x3), dim=1)
+        x = self.dropout(x)
+        return self.fc(x)
+
+model_name = "hfl/chinese-macbert-base"
+tokenizer = BertTokenizer.from_pretrained("bert_cnn_tokenizer")
+bert_model = BertModel.from_pretrained(model_name)
+model = BertCNNClassifier(bert_model).to(device)
+model.load_state_dict(torch.load("bert_cnn_sentiment.pth", map_location=device))
+model.eval()
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128).to(device)
+    with torch.no_grad():
+        outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"])
+        pred = torch.argmax(outputs, dim=1).item()
+    return id2label[pred]
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=3, placeholder="请输入朋友圈文案..."),
+    outputs="text",
+    title="朋友圈情绪识别",
+    description="输入一段朋友圈内容，判断情绪：负向 / 中性 / 正向"
+)
+
+interface.launch()

bert_cnn_sentiment.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a38a58db10a6d6b331a124ca39ca2ce2e6e7550afe3fd0b039fe8bfad0c505dc
+size 412847589

bert_cnn_sentiment_project.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:853b4c78d966db5ed56344ae06e825aa7c53d46c314f2646eca187416af4a358
+size 384000747

requirements.txt

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+torch
+transformers
+gradio
+scikit-learn
+pandas
+openpyxl
+tqdm